Burhan Ergen

Time–frequency analysis of phonocardiogram signals using wavelet transform: a comparative study

Analysis of phonocardiogram (PCG) signals provides a non-invasive means to determine the abnormalities caused by cardiovascular system pathology. In general, time–frequency representation (TFR) methods are used to study the PCG signal because it is one of the non-stationary bio-signals. The continuous wavelet transform (CWT) is especially suitable for the analysis of non-stationary signals and to obtain the TFR, due to its high resolution, both in time and in frequency and has recently become a favourite tool. It decomposes a signal in terms of elementary contributions called wavelets, which are shifted and dilated copies of a fixed mother wavelet function, and yields a joint TFR. Although the basic characteristics of the wavelets are similar, each type of the wavelets produces a different TFR. In this study, eight real types of the most known wavelets are examined on typical PCG signals indicating heart abnormalities in order to determine the best wavelet to obtain a reliable TFR. For this purpose, the wavelet energy and frequency spectrum estimations based on the CWT and the spectra of the chosen wavelets were compared with the energy distribution and the autoregressive frequency spectra in order to determine the most suitable wavelet. The results show that Morlet wavelet is the most reliable wavelet for the time–frequency analysis of PCG signals.

The analysis of heart sounds based on linear and high order statistical methods

This paper investigates the applicability of high order statistical autoregressive (AR-HOS) modeling method in analyzing biomedical signals. The autoregressive (AR) method using linear prediction and AR-HOS method using cumulants; are applied on normal and pathological heart sound signals. It is found that the AR-HOS modeling a signal produce a more accurate and higher resolution spectrum than AR modeling.

Texture based feature extraction methods for content based medical image retrieval systems

The developments of content based image retrieval (CBIR) systems used for image archiving are continued and one of the important research topics. Although some studies have been presented general image achieving, proposed CBIR systems for archiving of medical images are not very efficient. In presented study, it is examined the retrieval efficiency rate of spatial methods used for feature extraction for medical image retrieval systems. The investigated algorithms in this study depend on gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), and Gabor wavelet accepted as spatial methods. In the experiments, the database is built including hundreds of medical images such as brain, lung, sinus, and bone. The results obtained in this study shows that queries based on statistics obtained from GLCM are satisfied. However, it is observed that Gabor Wavelet has been the most effective and accurate method.

An investigation on magnetic imaging findings of the inner ear: A relationship between the internal auditory canal, its nerves and benign paroxysmal positional vertigo

Abstract Visualization of the inner ear has been performed using magnetic resonance imaging (MRI) to investigate benign paroxysmal positional vertigo (BPPV). In the patients with BPPV, our recent findings indicate the thickness of some internal auditory canal (IAC) nerves narrower than the thickness of healthy subjects. The thickness of the IAC and its nerves are measured using brain MRI images. The cross sectional area (CSA) of a nerve is assumed as its thickness. Some statistical measurement and a statistical classification are performed on the CSA data to investigate any relation between IAC, the nerves and BPPV.

Classification of face images using discrete cosine transform

In this study, it is aimed to determine whether a given image belongs to for that person. For feature extraction, which is an important part of pattern recognition, feature vector is obtained by using discrete cosine transform after performing preprocess the images on the current face. Based on the of datas obtained from conversion are classified by using 5%, 8%, 10%, and 15%. The nearest neighbor algorithm (KNN) is used in classification process. Face images consist of images that, taken from ORL database, belongs to 40 individuals, each has 10 different images. As a result, high success were obtained by using the few data.

A Fusion Method of Gabor Wavelet Transform and Unsupervised Clustering Algorithms for Tissue Edge Detection

This paper proposes two edge detection methods for medical images by integrating the advantages of Gabor wavelet transform (GWT) and unsupervised clustering algorithms. The GWT is used to enhance the edge information in an image while suppressing noise. Following this, the k-means and Fuzzy c-means (FCM) clustering algorithms are used to convert a gray level image into a binary image. The proposed methods are tested using medical images obtained through Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) devices, and a phantom image. The results prove that the proposed methods are successful for edge detection, even in noisy cases.

Eye Location and Eye State Detection in Facial Images Using Circular Hough Transform

Recently, eye states are used as inputs to various applications such as facial expression recognition systems, human-computer interaction and driver fatigue detection systems. Especially with the prominence of human computer interaction, eye state detection has drawn great attention in the past decade. In this study, an eye state detection system based on Circular Hough Transform (CHT) has been offered. Initially, face and eye images are extracted from given gray-level images. After some preprocessing steps, existence of circular iris structure is searched within the extracted eye image using CHT. Existence of circular iris structure is searched within the eye image with the help of circular Hough transform. Eyes are decided as open if iris could identified as a circle.

Characterization of phonocardiogram signals using bispectral estimation

The application of the bispectrum as a high-order spectrum (HOS) method in analyzing normal and pathological heart sound signal is investigated in this paper. The bispectrum of phonocardiogram (PCG) signals is used to characterize the abnormalities while detecting non-linearity or non-Gaussianity in order to improve the diagnostic performance. In the comparison of the diagonal slice of the bispectrum and the power spectrum of PCG signal, the results have shown that the HOS reveal the differences in heart sound analysis indicating non-Gaussianity or non-linearity. It is concluded that the diagonal slice and the peaks of bispectrum are efficiently determine the cardiac dysfunctions effecting on heart sounds.

Evaluation of Internal Auditory Canal Structures in Tinnitus of Unknown Origin

Objectives The aim of the present study was to evaluate the internal auditory canal (IAC) and the nerves inside it to define possible structural differences in cases with subjective tinnitus of unknown origin. Methods Cases applying to the ear, nose and throat department with the complaint of tinnitus with unknown origin and having normal physical examination and test results were included in the study (n=78). Patients admitted to the radiology clinic for routine cranial magnetic resonance imaging (MRI) and whose MRI findings revealed no pathologies were enrolled as the control group (n=79). Data for the control group were obtained from the radiology department and informed consent was obtained from all the patients. Diameters of the IAC and the nerves inside it were measured through enhanced images obtained by routine temporal bone MRIs in all cases. Statistical evaluations were performed using Student t-test and statistical significance was defined as P<0.05. Results Measurements of IAC diameters revealed statistically significant differences between the controls and the tinnitus group (P<0.05). Regarding the diameters of the cochlear nerve, facial nerve, inferior vestibular nerve, superior vestibular nerve, and total vestibular nerve, no statistically significant difference was found between the controls and the tinnitus group. Conclusion Narrowed IAC has to be assessed as an etiological factor in cases with subjective tinnitus of unknown origin.

Circular hough transform based eye state detection in human face images

Nowadays eye states are used as inputs to various applications such as facial expression recognition systems, human-computer interaction and driver fatigue detection systems. Especially with the pervasion of human computer interaction, eye state detection has drawn great attention in the past decade. In this study, an eye state detection system based on circular Hough transform has been offered. Initially, a face image is extracted from a given image. Eye pair images are obtained from this face image, and eyes are acquired from the eye pair images. After preprocessing, existence of circular iris structure is searched with the help of circular Hough transfrom within the eye image. Eyes are decided as open if iris is visible.